DynaFlow lets a main agent synthesize multi-agent workflows, hits 88.08% overall accuracy on six benchmarks

[HF Papers] The paper DynaFlow: Learning Free-Form Multi-Agent Orchestration, by Ruhao Liu, Qi Li, Shizun Wang and Xinchao Wang, introduces DynaFlow, a framework that integrates free-form executable workflow synthesis into the action space of the main agent. According to the authors, the agent decides when collaboration is needed and generates task-specific programs that coordinate sub-agents through sequential or parallel execution, structured information exchange and verification, with execution results and traces informing subsequent actions. The authors said only the main agent is trained, via supervised fine-tuning on distilled trajectories followed by refinement on examples filtered for unsupported premises, biased verification and information-flow defects, while all sub-agents remain fixed. They reported that across six benchmarks spanning mathematical reasoning, scientific question answering and multi-hop retrieval, DynaFlow achieves 88.08% overall accuracy in Forced mode, outperforming the strongest evaluated baseline by 10.90 percentage points with the same Qwen3.5-27B backbone. Workflow analysis found more explicit use, verification and refinement of upstream outputs after training, the authors said. This is a preprint and the findings have not been peer-reviewed.